音乐推荐系统的一些参考概述

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讲述了音乐推荐系统的做法,以及一些可供参考的做法
3 The ideal music recommender o maximize user's satisfaction o Recommend songs to hit top songs of User‘ s favourite list o Nowadays streaming music provides best services such as soundcloud, deezer Spotify Mithun madathil Spotify o Uses various ways of recommendation o 100 mio. monthly active users with millions of songs and playlists o Three main services for recommendation and a feedback system Mithun madathil Spotify track STARBOY Starboy 目3: Daft Punk Suchen 2. Party Monster Browse 4:09 Deine musik False alarm ZULETZT ABGESPIELT Starboy Remind 目3:38 5. Rockin 目3:52 PLAYLIST Grateful ALBUM Starboy 6. Secrets 4:25 Von The Weeknd Indian PLAYUST 2016·18s0NGs True colors PLAY Stargirl Interlude 1:51 Lana Del Rey SPE| CHERN· App installieren④ 9. Sidewalks 3:51 10 Six Feet IInd ) The Weekend 0:32 Spotify track danceability": 0.560 energy: 0.527 key loudness":-9783 mode 1 speechless 8.6374, acousticness:0.516 instrumentalness":0.0000240, liveness 8.156, valence:0. 336, tempo":93.441, type":audio features id":2z7D7kbpRcTvEdT71tdiNQ uri:spotify: track: 2z7D7kbpRcTvEdT71tdiNQ", trackhref":https://api.spotifycom/v1/tracks/2z7d7kbprctvedt71tdinq analysis_url":"http://echonest-analysis.s3.amazonaws.com/tr/-enytqjtfbuv9xjtxvpei2tp9pxqi7k 'duration ms": 168720 time signature": 4 Mithun madathil [5] 1. Content-based recommendation o Without user 's evaluation or ratings o Uses machine language to acquire information o Algorithms: decision trees, neural networks and vector-based methods Mithun madathil 2. Knowledge-based recommendation o Based on demands and preferences of user o Predictions decided by functions and features of objects Mithun madathil 3. Collaborative Filtering-KNN o Uses K-nearest neighbour(KNNI technique o Music taste of users calculates distance between different users o Search for neighbour users who share similar interest in music and recommend content o Daily life: friend's recommendation Mithun madathil Categories. Memory Model-based Hybrid based Predict items Uses Combining based on algorithms both models previoUs and models and ratings preferences outperforms them [2] Mithun madathil

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